Abstract
Shannon’s entropy measure quantifies our ignorance of a system in terms of surprise and probability. The measure of relative entropy, or Kullback-Leibler divergence, quantifies our surprise when trying to code a posterior probability distribution P when using a code derived from a prior probability distribution Q. This measure is relevant for interdisciplinary research where crossing disciplinary boundaries requires methodological and conceptual bridges. I present three constructive usages of this measure in linguistics (e.g., the measure of semantic transparency in linguistic compounds), sport (i.e., modeling the behavior of a soccer team), and the study of human interactions (i.e., identifying significant romantic relations in a successful TV series) and conclude by pointing to further usages in interdisciplinary research.
Original language | American English |
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Title of host publication | Elgar Encyclopedia of Interdisciplinarity and Transdisciplinarity |
Editors | Frédéric Darbellay |
Publisher | Edward Elgar Publishing Ltd. |
Pages | 426-429 |
Number of pages | 4 |
ISBN (Electronic) | 9781035317967 |
ISBN (Print) | 9781035317950 |
DOIs | |
State | Published - 1 Jan 2024 |
Keywords
- Entropy
- Interdisciplinary research
- Kullnack-Leibler divergence
- Relative entropy
All Science Journal Classification (ASJC) codes
- General Social Sciences
- General Arts and Humanities