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

• Main Authors: Fernandez, Nelson, Maldonado, Carlos, Gershenson, Carlos
• Format: Report
• Language: unknown
• Published: arXiv 2013
• Subjects: Adaptation and Self-Organizing Systems nlin.AO; Information Theory cs.IT; Other Quantitative Biology q-bio.OT; FOS Physical sciences; FOS Computer and information sciences; FOS Biological sciences; H.1.1; F.1.3; J.3; Arctic; Arctic Lake
• Online Access: https://dx.doi.org/10.48550/arxiv.1304.1842; https://arxiv.org/abs/1304.1842

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. : 35 pages, 12 figures, to be published in Prokopenko, M., editor, Guided Self-Organization: Inception. Springer. In Press