Abstract
The identification of system’s parts that rule its dynamics and the understanding of its dynamical organisation is a paramount objective in the analysis of complex systems. In previous work we have proposed the Dynamical Cluster Index method, which is based on information-theoretical measures. This method makes it possible to identify the components of a complex system that are relevant for its dynamics as well as their relation in terms of information flow. Complex systems’ organisation is often characterised by intertwined components. The detection of such dynamical structures is a prerequisite for inferring the hierarchical organisation of the system. The method relies on a ranking based on a statistical index, which depends on a reference system (the homogeneous system) generated according to a parametrised sampling procedure. In this paper we address the issue of assessing the robustness of the method against the homogeneous system generation model. The results show that the method is robust and can be reliably applied to the analysis of data from complex system dynamics in general settings, without requiring particular hypotheses.
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We will further discuss the consequences of this hypothesis.
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The frequency of 0 s is simply \(1-f_i\).
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As results are not distinguishable for the two models, results just concern model (i).
References
Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Poli, I., Serra, R.: On some properties of information theoretical measures for the study of complex systems. In: Pizzuti, C., Spezzano, G. (eds.) WIVACE 2014. CCIS, vol. 445, pp. 140–150. Springer, Heidelberg (2014)
Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Serra, R.: Exploring the organisation of complex systems through the dynamical interactions among their relevant subsets. In: Andrews, P., Caves, L., Doursat, R., Hickinbotham, S., Polack, F., Stepney, S., Taylor, T., Timmis, J. (eds.) Proceedings of the European Conference on Artificial Life 2015 - ECAL2015, pp. 286–293 (2015)
Gourévitch, B., Eggermont, J.: Evaluating information transfer between auditory cortical neurons. J. Neurophysiol. 97, 2533–2543 (2007)
Kendall, M.: Rank Correlation Methods. Hafner Publishing Co., New york (1955)
Lizier, J., Prokopenko, M.: Differentiating information transfer and causal effect. Eur. Phys. J. B 73(4), 605–615 (2010)
Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85(2), 461–464 (2000)
Serra, R., Villani, M., Graudenzi, A., Kauffman, S.: Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. J. Theor. Biol. 246(3), 449–460 (2007)
Serra, R., Villani, M., Semeria, A.: Genetic network models and statistical properties of gene expression data in knock-out experiments. J. Theor. Biol. 227, 149–157 (2004)
Shmulevich, I., Kauffman, S., Aldana, M.: Eukaryotic cells are dynamically ordered or critical but not chaotic. PNAS 102(38), 13439–13444 (2005)
Tononi, G., McIntosh, A., Russel, D., Edelman, G.: Functional clustering: identifying strongly interactive brain regions in neuroimaging data. Neuroimage 7, 133–149 (1998)
Villani, M., Barbieri, A., Serra, R.: A dynamical model of genetic networks for cell differentiation. PLoS ONE 6, e17703 (2011)
Villani, M., Benedettini, S., Roli, A., Lane, D., Poli, I., Serra, R.: Identifying emergent dynamical structures in network models. In: Bassis, S., Esposito, A., Morabito, F.C. (eds.) Recent Advances of Neural Networks Models and Applications. SIST, vol. 26, pp. 3–13. Springer, Heidelberg (2014)
Villani, M., Filisetti, A., Benedettini, S., Roli, A., Lane, D., Serra, R.: The detection of intermediate-level emergent structures and patterns. In: Liò, P., Miglino, O., Nicosia, G., Nolfi, S., Pavone, M. (eds.) Advances in Artificial Life, ECAL 2013, pp. 372–378. The MIT Press, Cambridge (2013). http://mitpress.mit.edu/books/advances-artificial-life-ecal-2013
Villani, M., Roli, A., Filisetti, A., Fiorucci, M., Poli, I., Serra, R.: The search for candidate relevant subsets of variables in complex systems. Artif. Life 21(4), 395–397 (2015)
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Villani, M., Carra, P., Roli, A., Filisetti, A., Serra, R. (2016). On the Robustness of the Detection of Relevant Sets in Complex Dynamical Systems. In: Rossi, F., Mavelli, F., Stano, P., Caivano, D. (eds) Advances in Artificial Life, Evolutionary Computation and Systems Chemistry. WIVACE 2015. Communications in Computer and Information Science, vol 587. Springer, Cham. https://doi.org/10.1007/978-3-319-32695-5_2
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