The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio

Riccardo Rossi, Michela Gelfusa, Filippo De Masi, Matteo Ossidi, Andrea Murari and M. De Aguiar

Complexity, 2021, vol. 2021, 1-14

Abstract: Understanding and quantifying the mutual influence between systems remain crucial but challenging tasks in any scientific enterprise. The Pearson correlation coefficient, the mutual information, and the information quality ratio are the most widely used indicators, only the last two being valid for nonlinear interactions. Given their limitations, a new criterion is proposed, the reciprocal influence criterion, which is very simple conceptually and does not make any assumption about the statistics of the stochastic variables involved. In addition to being normalised as the information quality ratio, it provides a much better resilience to noise and much higher stability to the issues related to the determination of the involved probability distribution functions. A conditional version, to counteract the effects of confounding variables, has also been developed, showing the same advantages compared to the more traditional indicators. A series of systematic tests with numerical examples is reported, to compare the properties of the new indicator with the more traditional ones, proving its clear superiority in practically all respects.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2021/9426547.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2021/9426547.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9426547

DOI: 10.1155/2021/9426547

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().