Simulations of agents in social networks harvesting a resource

Managing natural resources increasingly requires an understanding not only of the underlying resource dynamics but also the dynamics of human use. In an agent-based model, we simulate agents harvesting a renewable resource, and examine the effect of agents in different social networks on their ability to exploit the resource under different levels of uncertainty. When uncertainty in the resource is high, under assumed conditions, ordered social networks can exploit the resources better by passing information among the agents than when individuals act independently of each other. The more highly connected random networks, however, leads to lower aggregate harvests. When a single “skilled” agent is able to obtain a greater harvest than the others, a hierarchical performance among agents results, with those connected directly to the “skilled” agent obtaining higher harvests than those that are not.