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Unifying complexity and information.

Da-guan Ke¹

¹Department of Biomedical Engineering, Wenzhou Medical College, Wenzhou 325035, China. kdg@wzmc.edu.cn

Scientific Reports

|April 6, 2013

Summary

This summary is machine-generated.

This study introduces a unified complexity measure for complex systems, resolving long-standing disputes between different criteria. It establishes a universal framework applicable to deterministic systems, statistical mechanics, and living organisms.

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Area of Science:

Computer science
Life sciences
Social sciences
Physical sciences

Background:

Complex systems lack a universally accepted fundamental complexity measure, unlike Shannon entropy (H) in statistical mechanics.
Superficially conflicting complexity-randomness (C-R) criteria have led to adaptable measures, but their underlying causes remain unclear.

Purpose of the Study:

To clarify the root causes of conflicting complexity measurement criteria.
To develop a general information measure that unifies different complexity criteria.
To establish a single framework for analyzing complex systems across various scientific domains.

Main Methods:

Tracing representative and adaptable complexity measures to their specific universal data-generating or -regenerating models (UDGM/UDRM).
Identifying a counterpart to Shannon entropy for deterministic dynamical systems.
Developing a specific UDRM that enables intrinsic adaptability for a general information measure.

Main Results:

The root of each complexity measure is linked to a particular UDGM or UDRM.
A complexity measure for deterministic systems is established as a counterpart to Shannon entropy for random processes.
A general information measure is proposed, resolving major disputes in complexity measurement.

Conclusions:

A unified approach to complexity measurement is achieved by linking measures to data-generating models.
The proposed general information measure provides a consistent framework for diverse complex systems.
This work bridges deterministic systems, statistical mechanics, and biological complexity.