Where to go goose hunting? Using pattern-oriented modeling to better understand human decision processes

ABSTRACTTo predict hunting pressure at a regional level for the adaptive harvest management of a European goose population, we created a predictive model within an existing agent-based model framework. In this paper, we outline the inputs, outputs, and learning from developing this model, using pattern-oriented modeling (POM), to predict the regional distribution of goose hunting locations. Our results showed that social aspects (e.g., crowding, how far hunters are prepared to travel) may influence hunter decisions when choosing hunting locations. However, access to multiple hunting locations and knowledge of goose behavior and likely foraging areas were more important decision drivers. A crucial model outcome was the secondary prediction of the size of the potential pool of goose hunters. We believe that POM is a beneficial framework for those wishing to define, test, and ultimately develop better predictive models of human decision-making and subsequent behaviors and feedbacks.