Open access publications in the SLU publication database

Abstract

Despite improved knowledge and stricter regulations, numerous fish stocks remain overharvested. Previous research has shown that fisheries management may fail when the models and assessments used to inform management are based on unrealistic assumptions regarding fishers' decision-making and responses to policies. Improving the understanding of fisher behaviour requires addressing its diversity and complexity through the integration of social science knowledge into modelling. In our paper, we review and synthesize state-of-the-art research on both social science's understanding of fisher behaviour and the representation of fisher decision-making in scientific models. We then develop and experiment with an agent-based social-ecological fisheries model that formalizes three different fishing styles. Thereby we reflect on the implications of our incorporation of behavioural diversity and contrast it with the predominant assumption in fishery models: fishing practices being driven by rational profit maximizing. We envision a next generation of fisheries models and management that account for social scientific knowledge on individual and collective human behaviours. Through our agent-based model, we demonstrate how such an integration is possible and propose a scientific approach for reducing uncertainty based on human behavioural diversity in fisheries. This study serves to lay the foundations for a next generation of social-ecological fishery models that account for human behavioural diversity and social and ecological complexity that are relevant for a realistic assessment and management of fishery sustainability problems.