Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis

Nelson Fernandez; Carlos Maldonado; Carlos Gershenson

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arXiv · 1304.1842

Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis

Nelson Fernandez·Carlos Maldonado·Carlos Gershenson

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Abstract

This chapter reviews 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 a system or process produces. 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 different scales, which can be studied with multi-scale profiles.

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Nelson Fernandez, Carlos Maldonado, Carlos Gershenson. 2013-07-31. Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis. https://arxiv.org/abs/1304.1842

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