Abstract. It has been demonstrated that social learning can enable agents to discover and maintain behaviours that are inaccessible to incremental genetic evolution alone. However, previous models investigating the ability of social learning to provide access to these inaccessible behaviours are often limited. Here we investigate teacher-learner social learning strategies. It is often the case that teachers in teacher-learner social learning models are restricted to one type of agent, be it a parent or some fit individual; here we broaden this exploration to include a variety of teachers to investigate whether these social learning strategies are also able to demonstrate access to, and maintenance of, behaviours inaccessible to incremental genetic evolution. In this work new agents learn from either a parent, the fittest individual, the oldest individual, a random individual or another young agent. Agents are tasked with solving a river crossing task, with new agents learning from a teacher in mock evaluations. The behaviour necessary to successfully complete the most difficult version of the task has been shown to be inaccessible to incremental genetic evolution alone, but achievable using a combination of social learning and noise in the Genotype-Phenotype map. Here we show that this result is robust in all of the teacher-learner social learning strategies explored here.

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.

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