Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information
Pairwise interactions are critical to collective dynamics of natural and technological systems. Information theory is the gold standard to study these interactions, but recent work has identified pitfalls in the way information flow is appraised through classical metrics—time-delayed mutual informat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | Journal of Physics: Complexity |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-072X/acace0 |
| _version_ | 1797844609301217280 |
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| author | Pietro De Lellis Manuel Ruiz Marín Maurizio Porfiri |
| author_facet | Pietro De Lellis Manuel Ruiz Marín Maurizio Porfiri |
| author_sort | Pietro De Lellis |
| collection | DOAJ |
| description | Pairwise interactions are critical to collective dynamics of natural and technological systems. Information theory is the gold standard to study these interactions, but recent work has identified pitfalls in the way information flow is appraised through classical metrics—time-delayed mutual information and transfer entropy. These pitfalls have prompted the introduction of intrinsic mutual information to precisely measure information flow. However, little is known regarding the potential use of intrinsic mutual information in the inference of directional influences to diagnose interactions from time-series of individual units. We explore this possibility within a minimalistic, mathematically tractable leader–follower model, for which we document an excess of false inferences of intrinsic mutual information compared to transfer entropy. This unexpected finding is linked to a fundamental limitation of intrinsic mutual information, which suffers from the same sins of time-delayed mutual information: a thin tail of the null distribution that favors the rejection of the null-hypothesis of independence. |
| first_indexed | 2024-04-09T17:26:04Z |
| format | Article |
| id | doaj.art-7b75b161e5154b8c99c5f37347c9b570 |
| institution | Directory Open Access Journal |
| issn | 2632-072X |
| language | English |
| last_indexed | 2024-04-09T17:26:04Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics: Complexity |
| spelling | doaj.art-7b75b161e5154b8c99c5f37347c9b5702023-04-18T13:50:52ZengIOP PublishingJournal of Physics: Complexity2632-072X2022-01-014101500110.1088/2632-072X/acace0Inferring directional interactions in collective dynamics: a critique to intrinsic mutual informationPietro De Lellis0https://orcid.org/0000-0002-2656-6454Manuel Ruiz Marín1https://orcid.org/0000-0001-9228-6410Maurizio Porfiri2https://orcid.org/0000-0002-1480-3539Department of Electrical Engineering and Information Technology , University of Naples Federico II, Naples 80125, ItalyDepartment of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena , Cartagena, Murcia 30201, SpainCenter for Urban Science and Progress, Department of Mechanical and Aerospace Engineering, and Department of Biomedical Engineering, New York University Tandon School of Engineering , Brooklyn, NY 11201, United States of AmericaPairwise interactions are critical to collective dynamics of natural and technological systems. Information theory is the gold standard to study these interactions, but recent work has identified pitfalls in the way information flow is appraised through classical metrics—time-delayed mutual information and transfer entropy. These pitfalls have prompted the introduction of intrinsic mutual information to precisely measure information flow. However, little is known regarding the potential use of intrinsic mutual information in the inference of directional influences to diagnose interactions from time-series of individual units. We explore this possibility within a minimalistic, mathematically tractable leader–follower model, for which we document an excess of false inferences of intrinsic mutual information compared to transfer entropy. This unexpected finding is linked to a fundamental limitation of intrinsic mutual information, which suffers from the same sins of time-delayed mutual information: a thin tail of the null distribution that favors the rejection of the null-hypothesis of independence.https://doi.org/10.1088/2632-072X/acace0information flowstatistical inferenceintrinsic mutual informationtransfer entropytime-series analysiscollective behavior |
| spellingShingle | Pietro De Lellis Manuel Ruiz Marín Maurizio Porfiri Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information Journal of Physics: Complexity information flow statistical inference intrinsic mutual information transfer entropy time-series analysis collective behavior |
| title | Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information |
| title_full | Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information |
| title_fullStr | Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information |
| title_full_unstemmed | Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information |
| title_short | Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information |
| title_sort | inferring directional interactions in collective dynamics a critique to intrinsic mutual information |
| topic | information flow statistical inference intrinsic mutual information transfer entropy time-series analysis collective behavior |
| url | https://doi.org/10.1088/2632-072X/acace0 |
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