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

TL;DR

This paper reviews information-theoretic measures for complexity, emergence, self-organization, homeostasis, and autopoiesis, demonstrating their application in biological and ecological systems through case studies.

Contribution

It introduces axiomatic definitions of these measures and applies them to real-world systems, enabling multi-scale analysis of complex phenomena.

Findings

Emergence quantified as information produced by systems.

Self-organization defined as the inverse of emergence.

Autopoiesis ratio indicates system-environment complexity relationship.

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.