The effect of social learning in a small population facing environmental change: an agent-based simulation

Abstract

Learning is defined as behavioral modification due to experience, social or asocial. Social learning might be less costly than asocial learning and allow the rapid accumulation of learned traits across generations. However, the benefits of social learning in a small population of individuals relying on local interactions and experiencing environmental change are not well understood yet. In this study, we used agent-based simulations to address this issue by comparing the performance of social learning to asocial learning and innate behavior, in both a static and a changing environment. Learning was modeled using neural networks, and innate behavior was modeled using genetically coded behaviors. The performance of 10 mobile simulated agents was measured under three environmental scenarios: static, abrupt change and gradual change. We found that social learning allows for a better performance (in terms of survival) than asocial learning in static and abrupt-change scenarios. In contrast, when changes are gradual, social learning delays achieving the correct alternative, while asocial learning facilitates innovation; interestingly, a mixed population (social and asocial learners) performs the best.