This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Wiser MJ, Dolson EL, Vostinar A, Lenski RE, Ofria C.2018. The Boundedness Illusion: Asymptotic projections from early evolution underestimate evolutionary potential. PeerJ Preprints6:e27246v2https://doi.org/10.7287/peerj.preprints.27246v2
Open-ended evolution researchers seek to create systems that continually produce new evolutionary outcomes, attempting to reflect the power and diversity of evolution in nature. The specific metrics used (novelty, complexity, diversity, etc) vary by researcher, but the holy grail would be a system where any of these can accumulate indefinitely. Of course, one challenge that we face in reaching this goal is even recognizing if we have succeeded. To determine the evolutionary potential of a system, we must conduct finite experiments; based on their results we can predict how we would expect evolution to progress were it to continue. Here we examine how such predictions might be made and how accurate they might be. We focus on predicting fitness; this metric is often easy to calculate, and correlated with increases in traits like novelty and complexity. For each run in a simple digital evolution experiment, we find the best fit to measured values of fitness, and demonstrate that projecting this fit out usually predicts that fitness will be constrained by an asymptote. Upon extending the experiment, however, we see that fitness often far exceeds this asymptote, belying the boundedness that it implies. Extending past a premature end point allows us to see beyond this "boundedness illusion"
We have made minor grammatical edits, and updated a reference to another preprint now that it has a DOI.
Analysis script (to be run in R) that reads in the raw data and outputs all figures and analyses