Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis
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.
Keywords
Cite
@article{arxiv.1304.1842,
title = {Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis},
author = {Nelson Fernandez and Carlos Maldonado and Carlos Gershenson},
journal= {arXiv preprint arXiv:1304.1842},
year = {2013}
}
Comments
35 pages, 12 figures, to be published in Prokopenko, M., editor, Guided Self-Organization: Inception. Springer. In Press