Collective Intelligence (CI) is an amplified, meta-intelligence that emerges when a distributed collective of individuals aggregate their inputs in order to solve a problem, often with the help of communication or knowledge pooling. Importantly, CI outcomes (e.g., solutions, decisions, judgments, wisdom and knowledge) are generally more problem-adequate and therefore seem more "intelligent" than the contribution of any solitary member. CI in human societies can therefore solve key and pressing problems that no individual can resolve alone. Importantly, with recent advances in digital technologies, we now have more potential to harness the full power of human collectives to better address fast-evolving, complex problems facing human societies, many of which are complex issues that are resulted from the interactions between humans and natural ecosystems. Problems like anthropogenic environmental changes, biodiversity loss, and overconsumption of natural resources, which often take place in so called social-ecological systems (SESs), requires adequate knowledge and complete understandings about complex relationships between intertwined social and environmental dimensions. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations. This dissertation explores the potentials of using CI approaches to leverage the local knowledge of environmental and natural resources stakeholders to better understand SESs, develop adequate knowledge of complex human-environment interactions, and inform sustainability decisions.First, this dissertation synthetizes key insights from biological, cognitive, behavioral, and management sciences literature to develop a framework that guides the design and generation of CI in human groups. This framework organizes fundamental design elements of CI and thus can help researchers, communities, and policymakers, especially in data-poor situations, design crowd-based approaches to aggregating knowledge of local people and stakeholders in order to achieve accurate and reliable understandings of complex human-environment interactions. Additionally, this dissertation empirically tests CI approaches using three real-world fisheries case studies. The first empirical study uses an example of inland freshwater pike fisheries to explore how CI of local stakeholders can be harnessed through aggregation of their mental models about human-environment interactions. This study shows that the aggregated model can provide scientifically sound insights about how the ecosystem and humans are coupled, and how their interactions are influenced by various management strategies. The second empirical study uses an example of striped bass fisheries in Massachusetts, to explore the impact of knowledge diversity on the CI of local stakeholders while pooling their local knowledge about the complex human-environment interactions. The final study uses an example of U.S. Atlantic coasts to scale up these CI approaches by crowdsourcing inputs from a very large population of local fishing communities to predict people's perception of, and behavioral responses to climate change impacts on ocean fisheries across a large social and ecological gradient. This study demonstrates perfect match among stakeholder-driven perceptions, their mental models' predictions of behavioral changes, and empirical patterns of climate change disturbances.In conclusion, this work demonstrates that CI approaches to utilizing stakeholders' local knowledge for understanding the complexity of SESs have considerable implications for dealing with scientific and management uncertainties, while many untapped potentials still remain.
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