Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3738
Publisher DOI: 10.3390/e22101107
arXiv ID: 2008.11430v2
Title: Complexity as causal information integration
Language: English
Authors: Langer, Carlotta 
Ay, Nihat 
Keywords: Causality; Complexity; Conditional independence; Em-Algorithm; Integrated information; Statistics - Methodology; Statistics - Methodology; Computer Science - Information Theory; Mathematics - Information Theory
Issue Date: 30-Sep-2020
Publisher: MDPI
Source: Entropy 22 (10): 1107 (2020)
Abstract (english): 
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.
URI: http://hdl.handle.net/11420/10192
DOI: 10.15480/882.3738
ISSN: 1099-4300
Journal: Entropy 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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