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.
Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis
Nelson Fernandez, Carlos Maldonado, Carlos Gershenson
New draft: Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis
In this chapter, concepts related to information and computation are reviewed in the context of human computation. A brief introduction to information theory and different types of computation is given. Two examples of human computation systems, online social networks and Wikipedia, are used to illustrate how these can be described and compared in terms of information and computation.
Full text at http://arxiv.org/abs/1304.1428
Draft of a chapter to be published in Michelucci, P. (Ed.) Handbook of Human Computation, Springer.