Ellerman, David (2009) Counting Distinctions: On the Conceptual Foundations of Shannon's Information Theory. Synthese, 168 (1). pp. 119149. ISSN 15730964

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Abstract
Categorical logic has shown that modern logic is essentially the logic of subsets (or "subobjects"). Partitions are dual to subsets so there is a dual logic of partitions where a "distinction" [an ordered pair of distinct elements (u,u′) from the universe U ] is dual to an "element". An element being in a subset is analogous to a partition π on U making a distinction, i.e., if u and u′ were in different blocks of π. Subset logic leads to finite probability theory by taking the (Laplacian) probability as the normalized size of each subsetevent of a finite universe. The analogous step in the logic of partitions is to assign to a partition the number of distinctions made by a partition normalized by the total number of ordered pairs U² from the finite universe. That yields a notion of "logical entropy" for partitions and a "logical information theory." The logical theory directly counts the (normalized) number of distinctions in a partition while Shannon's theory gives the average number of binary partitions needed to make those same distinctions. Thus the logical theory is seen as providing a conceptual underpinning for Shannon's theory based on the logical notion of "distinctions."
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Item Type:  Published Article or Volume  

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Keywords:  logical entropy, distinctions, Shannon entropy, logical information theory  
Subjects:  Specific Sciences > Computation/Information General Issues > History of Philosophy of Science Specific Sciences > Mathematics Specific Sciences > Probability/Statistics 

Depositing User:  David Ellerman  
Date Deposited:  23 Dec 2011 00:46  
Last Modified:  23 Dec 2011 00:46  
Item ID:  8967  
Journal or Publication Title:  Synthese  
Publisher:  Springer (Springer Science+Business Media B.V.)  
Subjects:  Specific Sciences > Computation/Information General Issues > History of Philosophy of Science Specific Sciences > Mathematics Specific Sciences > Probability/Statistics 

Date:  May 2009  
Page Range:  pp. 119149  
Volume:  168  
Number:  1  
ISSN:  15730964  
URI:  https://philsciarchivedev.library.pitt.edu/id/eprint/8967 
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