In dynamic decision tasks, the situations we confront are never the same: the world is constantly changing. Generally, our ability to generalize learned skills depends on the similarity between the learned skills and the situations in which we will apply those skills. However, in dynamic tasks, the situations we are trained in will most likely be different from the situations in which we need to apply skills. For example, in the face of emergencies, one could be trained to handle hypothetical disaster scenarios, but remain unprepared for the emergency that is actually experienced. This raises an important question: how can we best prepare for the unexpected? Cognitive science research suggests that heterogeneity during training helps people adapt to unexpected situations. However, evidence for a general diversity hypothesis is limited. In this research, we investigate this Diversity Hypothesis using a cognitive model of learning and decisions from experience based on Instance-Based Learning (IBL) Theory. We focus on the concept of decision complexity to investigate whether confronting decisions of diverse complexities results in improved adaptation to unexpected decision complexities, compared to situations of constant decision complexity. We conduct a simulation experiment using an IBL model in a Gridworld task, and expose agents to various degrees of diversity as they learn; we then observe how these agents transfer their acquired knowledge to a situation of novel decision complexity. Our results support the Diversity Hypothesis and the benefits of diversity on adaptation.