Derangetropy in Probability Distributions and Information Dynamics
Masoud Ataei, Xiaogang Wang, Yong Deng, Andrei Khrennikov · 2024
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Description
Entropy is usually a single number summarising a probability distribution, and Masoud Ataei and co-authors argue that a single number hides too much. They introduce derangetropy, a new measure that behaves like a function rather than a scalar, tracing how information is spread across the whole range of a distribution. Built with self-referential and periodic properties, it connects to differential equations and equilibrium states.
The authors support the idea with combinatorial reasoning and hands-on analysis, showing how derangetropy depicts the way a distribution behaves and evolves over time. Where Shannon entropy gives one summary figure, derangetropy aims to show the shape of information itself as it moves. They present it as a fresh tool for studying complex, layered systems in information theory, where how information is arranged can matter as much as how much of it there is.
Catalog record
| CREATOR | Masoud Ataei, Xiaogang Wang, Yong Deng, Andrei Khrennikov |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |