Recently, theoreticians have hypothesized that diverse groups, as opposed to groups that are homogeneous, may have relative merits [S. E. Page, The Diversity Bonus (2019)]—all of which lead to more success in solving complex problems. As such, understanding complex, intertwined environmental and social issues may benefit from the integration of diverse types of local expertise. However, efforts to support this hypothesis have been frequently made through laboratory-based or computational experiments, and it is unclear whether these discoveries generalize to real-world complexities. To bridge this divide, we combine an Internet-based knowledge elicitation technique with theoretical principles of collective intelligence to design an experiment with local stakeholders. Using a case of striped bass fisheries in Massachusetts, we pool the local knowledge of resource stakeholders represented by graphical cognitive maps to produce a causal model of complex social-ecological interdependencies associated with fisheries ecosystems. Blinded reviews from a scientific expert panel revealed that the models of diverse groups outranked those from homogeneous groups. Evaluation via stochastic network analysis also indicated that a diverse group more adequately modeled complex feedbacks and interdependencies than homogeneous groups. We then used our data to run Monte Carlo experiments wherein the distributions of stakeholder-driven cognitive maps were randomly reproduced and virtual groups were generated. Random experiments also predicted that knowledge diversity improves group success, which was measured by benchmarking group models against an ecosystem-based fishery management model. We also highlight that diversity must be moderated through a proper aggregation process, leading to more complex yet parsimonious models. Significance Groups can collectively achieve an augmented cognitive capability that enables them to effectively tackle complex problems. Importantly, researchers have hypothesized that this group property—frequently known as collective intelligence—may be improved in functionally more diverse groups. This paper illustrates the importance of diversity for representing complex interdependencies in a social-ecological system. In an experiment with local stakeholders of a fishery ecosystem, groups with higher diversity—those with well-mixed members from diverse types of stakeholders—collectively produced more complex models of human–environment interactions which were more closely matched scientific expert opinions. These findings have implications for advancing the use of local knowledge in understanding complex sustainability problems, while also promoting the inclusion of diverse stakeholders for increasing management success.

Systems thinking (ST) skills are often the foundation of sustainability science curricula. Though ST skill sets are used as a basic approach to reasoning about complex environmental problems, there are gaps in our understanding regarding the best ways to promote and assess ST learning in classrooms. Since ST learning provides Science, Technology, Engineering, and Mathematics (STEM) students’ important skills and awareness to participate in environmental problem-solving, addressing these gaps is an important STEM learning contribution. We have created guidelines for teaching and measuring ST skills derived from a hybrid of a literature review and through case study data collection. Our approach is based on semi-quantitative cognitive mapping techniques meant to support deep reasoning about the complexities of social–ecological issues. We begin by arguing that ST should be evaluated on a continuum of understanding rather than a binary of correct/incorrect or present/absent. We then suggest four fundamental dimensions of teaching and evaluating ST which include: (1) system structure, (2) system function, (3) identification of leverage points for change, and (4) trade-off analysis. Finally, we use a case study to show how these ideas can be assessed through cognitive maps to help students develop deep system understanding and the capacity to propose innovative solutions to sustainability problems.

In the wake of the COVID-19 pandemic small businesses made headlines as hard hit by customer losses, revenue declines, and business closures. Yet, the impacts have been felt disproportionately by small businesses that suffered interruption due to pre-existing socioeconomic stressors and/or concurrent natural hazards experienced during the pandemic. To illuminate those compound impacts, we conducted a survey of over 1350 U S.-based small businesses. Our findings indicate that those businesses that experienced concurrent natural hazards during the pandemic were associated with relatively greater negative impacts. But importantly, enterprises that are historically underrepresented group operated (HUGO)—minority, women, and veteran-operated businesses— saw largely amplified negative impacts from COVID-19. In terms of the magnitude of COVID-19 impacts, the effect size of belonging to HUGO was more than twice as large as the effect size of experiencing a concurrent natural hazard during the pandemic. These results provide evidence for the disproportionate impacts that HUGOs face due to the pandemic, which are exacerbated when compounded by natural hazards. Given these results, there is evidence that the opportunity gap between HUGO and non-HUGO businesses is significant ahead of additional stressors or shocks. This opportunity gap is further accelerated when compounded with other events, here the compounding of natural hazards and COVID-19. Additional interventions need to be offered to HUGO businesses in areas with high likelihood of overlapping incidents. Further work is required to address social inequity and economic fragility of HUGO businesses, especially those that face the complexity of additional shocks, such as natural hazards.