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[This book presents a new foundation for information theory where the notion of information is defined in terms of distinctions, differences, distinguishability, and diversity. The direct measure is logical entropy which is the quantitative measure of the distinctions made by a partition. Shannon entropy is a transform or re-quantification of logical entropy for Claude Shannon’s “mathematical theory of communications.” The interpretation of the logical entropy of a partition is the two-draw probability of getting a distinction of the partition (a pair of elements distinguished by the partition) so it realizes a dictum of Gian-Carlo Rota: ProbabilitySubsets≈InformationPartitions\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\frac {Probability}{Subsets}\approx \frac {Information}{Partitions}$$ \end{document}. Andrei Kolmogorov suggested that information should be defined independently of probability, so logical entropy is first defined in terms of the set of distinctions of a partition and then a probability measure on the set defines the quantitative version of logical entropy. We give a history of the logical entropy formula that goes back to Corrado Gini’s 1912 “index of mutability” and has been rediscovered many times.]
Published: Sep 2, 2021
Keywords: Information-as-distinctions; Logical entropy; History of the formula
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