The goal of Artificial Life research, as articulated by Chris Langton, is “to contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be.” The study and pursuit of open-ended evolution in artificial evolutionary systems exemplify this goal. However, open-ended evolution research is hampered by two fundamental issues: the struggle to replicate open-endedness in an artificial evolutionary system and our assumption that we only have one system (genetic evolution) from which to draw inspiration. We argue not only that cultural evolution should be seen as another real-world example of an open-ended evolutionary system but that the unique qualities seen in cultural evolution provide us with a new perspective from which we can assess the fundamental properties of, and ask new questions about, open-ended evolutionary systems, especially with regard to evolved open-endedness and transitions from bounded to unbounded evolution. Here we provide an overview of culture as an evolutionary system, highlight the interesting case of human cultural evolution as an open-ended evolutionary system, and contextualize cultural evolution by developing a new framework of (evolved) open-ended evolution. We go on to provide a set of new questions that can be asked once we consider cultural evolution within the framework of open-ended evolution and introduce new insights that we may be able to gain about evolved open-endedness as a result of asking these questions.

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.

In a recent article by Borg and Channon it was shown that social information alone, decoupled from any within-lifetime learning, can result in improved performance on a food-foraging task compared to when social information is unavailable. Here we assess whether access to social information leads to significant behavioral differences both when access to social information leads to improved performance on the task, and when it does not: Do any behaviors resulting from social information use, such as movement and increased agent interaction, persist even when the ability to discriminate between poisonous and non-poisonous food is no better than when social information is unavailable? Using a neuroevolutionary artificial life simulation, we show that social information use can lead to the emergence of behaviors that differ from when social information is unavailable, and that these behaviors act as a promoter of agent interaction. The results presented here suggest that the introduction of social information is sufficient, even when decoupled from within-lifetime learning, for the emergence of pro-social behaviors. We believe this work to be the first use of an artificial evolutionary system to explore the behavioral consequences of social information use in the absence of within-lifetime learning.

We describe the questions and discussions raised at the First Workshop on Social Learning and Cultural Evolution held at theArtificial Life Conference 2016 in Cancún, Mexico in July 2016. The purpose of the workshop was to assemble artificial life researchers interested in social learning and cultural evolution into one group so that we could focus on recent work and interesting open questions. Our discussion related to both the mechanisms of social learning and cultural evolution and the consequences and influence of social learning and cultural evolution on living systems. We present the contributions of our workshop presenters and conclude with a discussion of the more important open questions in this area.