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Complexity: against systems

Overview of attention for article published in Theory in Biosciences, February 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#29 of 210)
  • High Attention Score compared to outputs of the same age (88th percentile)

Mentioned by

twitter
10 X users
wikipedia
3 Wikipedia pages

Citations

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22 Dimensions

Readers on

mendeley
73 Mendeley
citeulike
1 CiteULike
Title
Complexity: against systems
Published in
Theory in Biosciences, February 2011
DOI 10.1007/s12064-011-0121-4
Pubmed ID
Authors

Dominique Chu

Abstract

This article assumes a specific intuitive notion of complexity as a difficulty to generate and/or assess the plausibility of models. Based on this intuitive understanding of complexity, it identifies two main causes of complexity, namely, radical openness and contextuality. The former is the idea that there are no natural systems. The modeler always needs to draw artificial boundaries around phenomena to generate feasible models. Contextuality is intimately connected to the requirement to simplify models and to leave out most aspects. Complexity occurs when contextuality and radical openness cannot be contained that is when it is not clear where the boundaries of the system are and which abstractions are the correct ones. This concept of complexity is illustrated using a number of example from evolution.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Netherlands 1 1%
Italy 1 1%
Vietnam 1 1%
Canada 1 1%
France 1 1%
Argentina 1 1%
Belarus 1 1%
Spain 1 1%
Other 1 1%
Unknown 62 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 27%
Student > Ph. D. Student 17 23%
Student > Master 8 11%
Professor > Associate Professor 6 8%
Student > Bachelor 3 4%
Other 10 14%
Unknown 9 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 21%
Computer Science 11 15%
Engineering 4 5%
Medicine and Dentistry 4 5%
Social Sciences 4 5%
Other 25 34%
Unknown 10 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 November 2021.
All research outputs
#3,535,777
of 24,287,697 outputs
Outputs from Theory in Biosciences
#29
of 210 outputs
Outputs of similar age
#21,629
of 190,206 outputs
Outputs of similar age from Theory in Biosciences
#2
of 3 outputs
Altmetric has tracked 24,287,697 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 210 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 190,206 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.