Measuring Complexity in an Aquatic Ecosystem

We apply formal measures of emergence, self-organization, homeostasis, autopoiesis and complexity to an aquatic ecosystem; in particular to the physiochemical component of an Arctic lake. These measures are based on information theory. Variables with an homogeneous distribution have higher values of emergence, while variables with a more heterogeneous distribution have a higher self-organization. Variables with a high complexity reflect a balance between change (emergence) and regularity/order (self-organization). In addition, homeostasis values coincide with the variation of the winter and summer seasons. Autopoiesis values show a higher degree of independence of biological components over their environment. Our approach shows how the ecological dynamics can be described in terms of information.Comment: 6 pages, to be published in Proceedings of the CCBCOL 2013, 2nd Colombian Computational Biology Congress, Springe

Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales

Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales.Comment: 42 pages, 11 figures, 2 table

Measuring the Complexity of Continuous Distributions

We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.Comment: 21 pages, 5 Tables, 4 Figure

Epitaxial self-organization: from surfaces to magnetic materials

Self-organization of magnetic materials is an emerging and active field. An overview of the use of self-organization for magnetic purposes is given, with a view to illustrate aspects that cannot be covered by lithography. A first set of issues concerns the quantitative study of low-dimensional magnetic phenomena (1D and 0D). Such effects also occur in microstructured and lithographically-patterned materials but cannot be studied in these because of the complexity of such materials. This includes magnetic ordering, magnetic anisotropy and superparamagnetism. A second set of issues concerns the possibility to directly use self-organization in devices. Two sets of examples are given: first, how superparamagnetism can be fought by fabricating thick self-organized structures, and second, what new or improved functionalities can be expected from self-organized magnetic systems, like the tailoring of magnetic anisotropy or controlled dispersion of properties.Comment: 13 pages, submitted in 2004. Part of a Special Issue about Self-organization on surfaces, published in C. R. Physiqu

Quantifying Self-Organization with Optimal Wavelets

The optimal wavelet basis is used to develop quantitative, experimentally applicable criteria for self-organization. The choice of the optimal wavelet is based on the model of self-organization in the wavelet tree. The framework of the model is founded on the wavelet-domain hidden Markov model and the optimal wavelet basis criterion for self-organization which assumes inherent increase in statistical complexity, the information content necessary for maximally accurate prediction of the system's dynamics. At the same time the method, presented here for the one-dimensional data of any type, performs superior denoising and may be easily generalized to higher dimensions.Comment: 12 pages, 3 figure

A Life experiment of development Mountain tourism in Portugal observed from the point of view of theories of Complexity, Complication and Self-organization

This paper is an attempt to use the ideas of deepening complexity and self organization theory to a life experiment in developing tourism in a Portuguese mountain region da Estrela.innovation diffusion; complexity; alternative choice; social innovation; learning process; tourism; portugal