English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the most fundamental problems in science is to define {\itquantitatively} the complexity of organized matters, i.e., {\it organizedcomplexity}. Although many measures have been proposed toward this aim inprevious decades, there is no agreed upon definition. This paper presents a newquantitative definition of organized complexity. This definition {\itsimultaneously} captures the three major features of complexity: computational(similar to logical depth), descriptional (similar to the Kolmogorov complexityand effective complexity) and distributional (similar to statisticalcomplexity). In addition, the proposed definition is computable and can measureboth probabilistic and deterministic forms of objects in a unified manner. Theproposed definition is based on circuits rather than Turing machines and$\epsilon$-machines. We give several criteria required for organized complexitymeasures and show that the proposed definition satisfies all of them for thefirst time. We then apply this quantitative definition to formulate a {\itsemantic information theory}. We present the first formal definition of a {\itsemantic information amount}, which is the core concept of the semanticinformation theory, that is based only on concretely defined notions. Previoussemantic information theories defined this amount under some a prioriinformation which is not concretely specified. We then unveil severalfundamental properties in the semantic information theory, e.g., a semanticsource coding theorem, semantic channel coding theorem, and effectivenesscoding theorem. Although the semantic information theory has a long history ofresearch going back more than six decades, there has been no study on itsrelation to organized complexity. This paper offers the first unified paradigmof organized complexity and semantic information theory.

A Unified Paradigm of Organized Complexity and Semantic Information Theory