Book Description

Human environments exhibit reliable and exploitable patterns of information, and extensive research in cognitive science demonstrates that evolution has selected for human decision strategies that are closely adapted to such environmental structures. Clustering is one such pervasive environmental structure (e.g., physical resources like food and abstract resources like information are clustered both across time and space) and in this dissertation, I explore the influence clustering wields on decision making. Studies 1 and 2 use cognitive modeling of human data to investigate recency (using only a recent set of experiences) as a decision strategy adapted to environmental clustering, and demonstrate that (1) people are relatively good at identifying and adapting their recency strategy to levels that are close to optimal in their clustered environments, and (2) people are generally biased toward a default recency strategy. While these two initial studies establish the influence of clustered environmental structures on decision making, real-world environmental structures are often simultaneously influenced by human decision making. For instance, patchiness of food resources (e.g., fish schools, vegetation patches) both influence the search strategies of predators and may also emerge as a result of these search strategies. Studies 3 and 4 use agent-based modeling to study dynamical mutual interactions between decision making and environmental clustering, demonstrating that they have regulatory influences on each other, producing self-organizing systems.