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Complexity as causal information integration
Citation Link: https://doi.org/10.15480/882.3738
Publikationstyp
Journal Article
Publikationsdatum
2020-09-30
Sprache
English
Author
Enthalten in
Volume
22
Issue
10
Start Page
1
End Page
32
Article Number
1107
Citation
Entropy 22 (10): 1107 (2020)
Publisher DOI
Scopus ID
ArXiv ID
Publisher
MDPI
Complexity measures in the context of the Integrated Information Theory of consciousness try to quantify the strength of the causal connections between different neurons. This is done by minimizing the KL-divergence between a full system and one without causal connections. Various measures have been proposed and compared in this setting. We will discuss a class of information geometric measures that aim at assessing the intrinsic causal influences in a system. One promising candidate of these measures, denoted by ΦCIS, is based on conditional independence statements and does satisfy all of the properties that have been postulated as desirable. Unfortunately it does not have a graphical representation which makes it less intuitive and difficult to analyze. We propose an alternative approach using a latent variable which models a common exterior influence. This leads to a measure ΦCII, Causal Information Integration, that satisfies all of the required conditions. Our measure can be calculated using an iterative information geometric algorithm, the em-algorithm. Therefore we are able to compare its behavior to existing integrated information measures.
Schlagworte
Causality
Complexity
Conditional independence
Em-Algorithm
Integrated information
Statistics - Methodology
Statistics - Methodology
Computer Science - Information Theory
Mathematics - Information Theory
DDC Class
004: Informatik