The complex, context-dependent, and dynamic nature of human behavior is increasingly recognized as both an important cause of sustainability problems and potential leverage for their solution. Human beings are diverse, as are the social, ecological, and institutional settings in which they are embedded. Despite this recognition and extensive knowledge about human decision-making in the behavioral sciences, empirical analysis, formal models, and decision support for sustainability policy in natural resource management often either neglect human behavior or are based on narrow and overly simplistic assumptions. Integrating insights from behavioral sciences into sustainability research and policy remains a challenge. This is in part due to the abundance and fragmentation of theories across the social sciences and in part the challenges of translating research across disciplines. We provide a set of tools to support the integration of knowledge about human behavior into empirical and model-based sustainability research. In particular, we (i) develop a process-oriented framework of embedded human cognition (Human Behavior-Cognition in Context or HuB-CC), (ii) select an initial set of 31 theories with the potential to illuminate behavior in natural resource contexts and map them onto the framework, and (iii) suggest pathways for using the framework and mapping to encourage trans-disciplinary investigations, identify and compare theories, and facilitate their integration into empirical research, formal models, and ultimately policy and governance for sustainability. Our theory selection, framework, and mapping offer a foundation—a “living” platform—upon which future collaborative efforts can build to create a resource for scholars and practitioners working at the intersection of social sciences and natural resource management.

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.