Options
The information theory of individuality
Citation Link: https://doi.org/10.15480/882.3755
Publikationstyp
Journal Article
Date Issued
2020-03-24
Sprache
English
TORE-DOI
Journal
Volume
139
Issue
2
Start Page
209
End Page
223
Citation
Theory in Biosciences 139 (2): 209-223 (2020-06-01)
Publisher DOI
Scopus ID
PubMed ID
32212028
Publisher
Springer
Despite the near universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. Here, we propose that individuals are aggregates that preserve a measure of temporal integrity, i.e., “propagate” information from their past into their futures. We formalize this idea using information theory and graphical models. This mathematical formulation yields three principled and distinct forms of individuality—an organismal, a colonial, and a driven form—each of which varies in the degree of environmental dependence and inherited information. This approach can be thought of as a Gestalt approach to evolution where selection makes figure-ground (agent–environment) distinctions using suitable information-theoretic lenses. A benefit of the approach is that it expands the scope of allowable individuals to include adaptive aggregations in systems that are multi-scale, highly distributed, and do not necessarily have physical boundaries such as cell walls or clonal somatic tissue. Such individuals might be visible to selection but hard to detect by observers without suitable measurement principles. The information theory of individuality allows for the identification of individuals at all levels of organization from molecular to cultural and provides a basis for testing assumptions about the natural scales of a system and argues for the importance of uncertainty reduction through coarse-graining in adaptive systems.
Subjects
Adaptation
Control
Evolution
Gestalt
Information decomposition
Mutual information
Shannon information
Shared information
Synergy
DDC Class
004: Informatik
Publication version
publishedVersion
Loading...
Name
Krakauer2020_Article_TheInformationTheoryOfIndividu.pdf
Size
1.79 MB
Format
Adobe PDF