2013/04/06 by Nelson Fernandez, Nelson Fernández, Carlos Eduardo Maldonado +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · Mathematics · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Artificial intelligence #Autopoiesis #Axiom #Biology #Computer science #Ecology #Economics #F.1.3 #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #H.1.1 #Hierarchy #Information Theory (cs.IT) #J.3 #Mathematics #Origins and Evolution of Life #Other Quantitative Biology (q-bio.OT) #Process (computing) #Self-organization #Sustainability and Ecological Systems Analysis #cs.IT #math.IT #nlin.AO #q-bio.OT
paper · pdf · doi:10.48550/arxiv.1304.1842
35 pages, 12 figures, to be published in Prokopenko, M., editor, Guided Self-Organization: Inception. Springer. In Press
openalex publication_date 2013/04/06 · arxiv created 2013/07/31 · arxiv updated 2013/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.