The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9426547 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561536644677632 |
|---|---|
| author | Riccardo Rossi Michela Gelfusa Filippo De Masi Matteo Ossidi Andrea Murari |
| author_facet | Riccardo Rossi Michela Gelfusa Filippo De Masi Matteo Ossidi Andrea Murari |
| author_sort | Riccardo Rossi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ef8da57027bd43ac94f6922dc6bab2f0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ef8da57027bd43ac94f6922dc6bab2f02025-02-03T01:24:49ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/94265479426547The Reciprocal Influence Criterion: An Upgrade of the Information Quality RatioRiccardo Rossi0Michela Gelfusa1Filippo De Masi2Matteo Ossidi3Andrea Murari4Department of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, ItalyConsorzio RFX (CNR, ENEA, INFN, Universita’ di Padova, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127 Padova, ItalyUnderstanding 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.http://dx.doi.org/10.1155/2021/9426547 |
| spellingShingle | Riccardo Rossi Michela Gelfusa Filippo De Masi Matteo Ossidi Andrea Murari The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio Complexity |
| title | The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio |
| title_full | The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio |
| title_fullStr | The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio |
| title_full_unstemmed | The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio |
| title_short | The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio |
| title_sort | reciprocal influence criterion an upgrade of the information quality ratio |
| url | http://dx.doi.org/10.1155/2021/9426547 |
| work_keys_str_mv | AT riccardorossi thereciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT michelagelfusa thereciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT filippodemasi thereciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT matteoossidi thereciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT andreamurari thereciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT riccardorossi reciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT michelagelfusa reciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT filippodemasi reciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT matteoossidi reciprocalinfluencecriterionanupgradeoftheinformationqualityratio AT andreamurari reciprocalinfluencecriterionanupgradeoftheinformationqualityratio |