Tree Structures: A Variational Approach to Shannon--Wiener Information
Hanno Hammer

TL;DR
This paper introduces a novel tree-structure-based framework that interprets Shannon-Wiener information as a minimization process over trees, linking entropy measures with topological concepts.
Contribution
It formalizes a new approach to entropy by connecting it to tree structures and topology, offering an intuitive and mathematical reinterpretation of information measures.
Findings
Shannon-Wiener information can be derived from minimizing a tree function.
Tree structures relate to neighborhood topologies in a set.
Entropy measures can be understood through a topological minimization process.
Abstract
Entanglement measures based on a logarithmic functional form naturally emerge in any attempt to quantify the degree of entanglement in the state of a multipartite quantum system. These measures can be regarded as generalizations of the classical Shannon-Wiener information of a probability distribution into the quantum regime. In the present work we introduce a previously unknown approach to the Shannon-Wiener information which provides an intuitive interpretation for its functional form as well as putting all entanglement measures with a similar structure into a new context: By formalizing the process of information gaining in a set-theoretical language we arrive at a mathematical structure which we call ''tree structures'' over a given set. On each tree structure, a tree function can be defined, reflecting the degree of splitting and branching in the given tree. We show in detail that…
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Taxonomy
TopicsQuantum Mechanics and Applications · Cognitive Science and Education Research · Neural dynamics and brain function
