Agent-Based Simulation Models in Fisheries Science

Abstract

The human dimension is one major source of uncertainty in the management of social-ecological systems such as fisheries. Agent-based models (ABMs) can help to reduce these uncertainties by making it possible to model and simulate human behavior. To understand how ABMs can be applied in fisheries science, a classification scheme was developed based on reviews in other social-ecological domains, theoretical frameworks, and documentation standards. This classification scheme was subsequently used to review agent-based simulation studies that modeled human decision-making in a fisheries context to identify trends and knowledge gaps. Applying the classification scheme revealed that the existing fisheries-related ABMs employ a variety of decision theories, policies, social interactions, agent memories, and data sources, and revealed a wide potential for applications of ABMs to a broad range of research questions and management recommendations. Nevertheless, it turned out that it is, so far, virtually unexplored how environmental factors influence fishing decisions or how social norms and learning influence fishing behavior. It also became clear that the documentation and provenance information of ABMs need to be improved–e.g., by applying standardized documentation procedures, such as ODD + D and TRACE–to enhance the credibility, transparency, and reusability of ABMs in fisheries science.