LAUSR

Abstract Learning is an important aspect of cognition that is crucial for the success of many species, and has been a factor involved in the evolution of distinct patterns of life history that depend on the environments in question. The extent to which different degrees of social and individual learning emerge follows from various species-dependent factors, such as the fidelity of information transmission between individuals, and that has previously been modelled in agent-based simulations with meme-based representations of learned knowledge and behaviours. A limitation of that previous work is that it was based on fixed environments, and it is known that different learning strategies will emerge depending on the variability of the environment. This paper will address that limitation by extending the existing modelling framework to allow the simulation of life history evolution and the emergence of appropriate learning strategies in changing environments.