The importance of noise colour in simulations of evolutionary systems

Abstract

Simulations of evolutionary dynamics often employ white noise as a model of stochastic environmental variation. Whilst white noise has the advantages of being simply generated and analytically tractable, empirical analyses demonstrate that most real environmental time series have power spectral densities consistent with pink or red noise, in which lower frequencies contribute proportionally greater amplitudes than higher frequencies. Simulated white noise environments may therefore fail to capture key components of real environmental time series, leading to erroneous results. To explore the effects of different noise colours on evolving populations, a simple evolutionary model of the interaction between life-history and the specialism-generalism axis was developed. Simulations were conducted using a range of noise colours as the environments to which agents adapted. Results demonstrate complex interactions between noise colour, reproductive rate, and the degree of evolved generalism; importantly, contradictory conclusions arise from simulations using white as opposed to red noise, suggesting that noise colour plays a fundamental role in generating adaptive responses. These results are discussed in the context of previous research on evolutionary responses to fluctuating environments, and it is suggested that Artificial Life as a field should embrace a wider spectrum of coloured noise models to ensure that results are truly representative of environmental and evolutionary dynamics.

Publication DOI: https://doi.org/10.1162/artl_a_00354
Additional Information: Copyright © 2022 Massachusetts Institute of Technology. This corrected proof version of an article published in Artificial LIfe at [https://doi.org/10.1162/artl_a_00354] is made available in accordance with the publisher's self-archiving policy.
Uncontrolled Keywords: Coloured noise,Evolutionary dynamics,Evolved generalism,Fluctuating environments,Life-history evolution,Variable environments,Biochemistry, Genetics and Molecular Biology(all),Artificial Intelligence
Publication ISSN: 1530-9185
Last Modified: 22 Apr 2024 07:28
Date Deposited: 22 May 2023 16:45
Full Text Link:
Related URLs: https://direct. ... -Simulations-of (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-03-16
Published Online Date: 2022-02-11
Accepted Date: 2021-11-10
Authors: Grove, Matt
Timbrell, Lucy
Jolley, Ben
Polack, Fiona
Borg, James M. (ORCID Profile 0000-0002-6662-0849)

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