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Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems

Overview of attention for article published in Scientific Reports, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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1 news outlet
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31 X users
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1 Google+ user
reddit
1 Redditor

Citations

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44 Dimensions

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85 Mendeley
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Title
Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems
Published in
Scientific Reports, April 2017
DOI 10.1038/s41598-017-00810-8
Pubmed ID
Authors

Alyssa Adams, Hector Zenil, Paul C. W. Davies, Sara Imari Walker

Abstract

Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) where the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.

X Demographics

X Demographics

The data shown below were collected from the profiles of 31 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Unknown 83 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 18%
Student > Ph. D. Student 12 14%
Student > Bachelor 9 11%
Other 7 8%
Professor 7 8%
Other 21 25%
Unknown 14 16%
Readers by discipline Count As %
Computer Science 15 18%
Agricultural and Biological Sciences 12 14%
Physics and Astronomy 8 9%
Social Sciences 5 6%
Mathematics 4 5%
Other 22 26%
Unknown 19 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 10 February 2023.
All research outputs
#1,312,869
of 24,814,419 outputs
Outputs from Scientific Reports
#12,825
of 135,784 outputs
Outputs of similar age
#25,902
of 315,311 outputs
Outputs of similar age from Scientific Reports
#474
of 4,225 outputs
Altmetric has tracked 24,814,419 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 135,784 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one has done particularly well, scoring higher than 90% 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 315,311 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 4,225 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.