In this section
@article{Rosas:2020:1751-8121/abb723,
author = {Rosas, FE and Mediano, PAM and Rassouli, B and Barrett, AB},
doi = {1751-8121/abb723},
journal = {Journal of Physics A: Mathematical and Theoretical},
pages = {485001--485001},
title = {An operational information decomposition via synergistic disclosure},
url = {http://dx.doi.org/10.1088/1751-8121/abb723},
volume = {53},
year = {2020}
}
TY - JOUR
AB - Multivariate information decompositions hold promise to yield insight into complex systems, and stand out for their ability to identify synergistic phenomena. However, the adoption of these approaches has been hindered by there being multiple possible decompositions, and no precise guidance for preferring one over the others. At the heart of this disagreement lies the absence of a clear operational interpretation of what synergistic information is. Here we fill this gap by proposing a new information decomposition based on a novel operationalisation of informational synergy, which leverages recent developments in the literature of data privacy. Our decomposition is defined for any number of information sources, and its atoms can be calculated using elementary optimisation techniques. The decomposition provides a natural coarse-graining that scales gracefully with the system's size, and is applicable in a wide range of scenarios of practical interest.
AU - Rosas,FE
AU - Mediano,PAM
AU - Rassouli,B
AU - Barrett,AB
DO - 1751-8121/abb723
EP - 485001
PY - 2020///
SN - 1751-8113
SP - 485001
TI - An operational information decomposition via synergistic disclosure
T2 - Journal of Physics A: Mathematical and Theoretical
UR - http://dx.doi.org/10.1088/1751-8121/abb723
UR - https://iopscience.iop.org/article/10.1088/1751-8121/abb723
UR - http://hdl.handle.net/10044/1/84139
VL - 53
ER -