Proceedings of the Annual Meeting of the Cognitive Science Society

Social learning is essential to survival. It is likely to evolve when it is more efficient than asocial, trial-and-error learning. The consensus in cultural evolutionary theory holds that some amount of environmental variability and uncertainty about the best decisions are necessary for social learning to evolve. However, current models for the evolution of social learning tend to conflate forms of uncertainty, and rarely consider different ones in tandem. Moreover, many models are limited by considering only two possible behaviors and environmental states. Here we use evolutionary agent-based modeling to identify the complex ways in which different forms of uncertainty affect social learning. We model a time-varying environment with dozens of possible behaviors performed by agents engaging in individual and social learning. We show that ambiguous payoffs, larger possible decision sets, and shorter agent lifespans sometimes increase social learning prevalence, as expected. However we also find which concrete uncertainty conditions cause evolution to select against social learning.