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.
Giorgio BertiniResearch on society, culture, art, neuroscience, cognition, critical thinking, intelligence, creativity, autopoiesis, self-organization, rhizomes, complexity, systems, networks, leadership, sustainability, thinkers, futures ++
4100 Posts in this Blog
- Follow Learning Change on WordPress.com