Advancing shannon entropy for measuring diversity in systems

Rajaram, Rajeev; Castellani, Brian; Wilson, AN

Abstract

From economic inequality and species diversity to power laws and the analysis of multiple trends and trajectories, diversity within systems is a major issue for science. Part of the challenge is measuring it. Shannon entropy has been used to rethink diversity within probability distributions, based on the notion of information. However, there are two major limitations to Shannon’s approach. First, it cannot be used to compare diversity distributions that have different levels of scale. Second, it cannot be used to compare parts of diversity distributions to the whole. To address these limitations, we introduce a renormalization of probability distributions based on the notion of case-based entropy as a function of the cumulative probability . Given a probability density , measures the diversity of the distribution up to a cumulative probability of , by computing the length or support of an equivalent uniform distribution that has the same Shannon information as the conditional distribution of up to cumulative probability . We illustrate the utility of our approach by renormalizing and comparing three well-known energy distributions in physics, namely, the Maxwell-Boltzmann, Bose-Einstein, and Fermi-Dirac distributions for energy of subatomic particles. The comparison shows that is a vast improvement over as it provides a scale-free comparison of these diversity distributions and also allows for a comparison between parts of these diversity distributions.

Citation

Rajaram, R., Castellani, B., & Wilson, A. (2017). Advancing shannon entropy for measuring diversity in systems. Complexity, 2017, Article 8715605. https://doi.org/10.1155/2017/8715605

• Journal Article Type: Article
• Acceptance Date: Apr 23, 2017
• Online Publication Date: May 24, 2017
• Publication Date: 2017
• Deposit Date: Sep 24, 2018
• Journal: Complexity
• Print ISSN: 1076-2787
• Electronic ISSN: 1099-0526
• Publisher: Hindawi
• Peer Reviewed: Peer Reviewed
• Volume: 2017
• Article Number: 8715605
• DOI: https://doi.org/10.1155/2017/8715605
• Public URL: https://durham-repository.worktribe.com/output/1313554