Complexes

Highlights • The useful of quantitative indicators of ecological complexity is evaluated. • Chaos should not be confused with complexity. • Light and temperature cause different ranges of complexity in the gradient. • Homoeostasis variation is related to the seasonal changes and transitions. • Autopoiesis reveals groups with higher and lower degree of autonomy. Abstract Measuring complexity is fast becoming a key instrument to compare different ecosystems at various scales in ecology. To date there has been little agreement on how to properly describe complexity in terms of ecology. In this regard, this manuscript assesses the significance of using a set of proposed measures based on information theory. These measures are as follows: emergence, self-organization, complexity, homeostasis and autopoiesis. A combination of quantitative and qualitative approaches was used in the data analysis with the aim to apply these proposed measures. This study system...

To improve the efficiency of peer-to-peer (P2P) systems while adapting to changing environmental conditions, static peer-to-peer protocols can be replaced by adaptive plans. The resulting systems are inherently complex, which makes their development and characterization a challenge for traditional methods. Here we propose the design and analysis of adaptive P2P systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. These measures allow the evaluation of adaptive P2P systems and thus can be used to guide their design. We evaluate the proposal with a P2P computing system provided with adaptation mechanisms. We show the evolution of the system with static and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, which correspond to a high variance in time of the measured complexity. Co...

In this chapter review measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem. Emergence is defined as the information produced by a system or process. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at multiple scales, which can be studied with multi-scale profiles. Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis Nelson Fernandez, Carlos Maldonado, Carlos Gershenson http://arxiv.org/abs/1304.1842

Complexity and Information: Measuring Emergence, Self-organization, Homeostasis, and Autopoiesis at Multiple Scales Keynote talk at the 5th International Workshop on Guided Self-Organization . University of Sydney, Australia, September 26th, 2012. youtu.be/Ba0zSNYkWtw?a   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. We use information theory to provide abstract and concise measures of complexity, emergence, self-organization, homeostasis, and autopoiesis. 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. Autopoiesis can be measured as the ratio between the ...

Ultra-large scale (ULS) systems are becoming pervasive. They are inherently complex, which makes their design and control a challenge for traditional methods. Here we propose the design and analysis of ULS systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. We evaluate the proposal with a ULS computing system provided with genetic adaptation mechanisms. We show the evolution of the system with stable and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, that correspond to a high variance in time of the measured complexity. Conversely, if the adaptive plan is less "aggressive", the system may be more stable, but the optimal performance may not be achieved. Measuring the Complexity of Ultra-Large-Scale Evolutionary Systems, Michele Amoretti, Carlos Gershenson. Subm...

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 (focussing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales. Gershenson, C. & N. Fernández (2012). Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales. C3 Report 2012.03.  http://arxiv.org/abs/1205.2026