462
To address these interrelated challenges, processes that are decision-driven and
participatory have been co-developed and implemented, which make research more
responsive to decision-making needs in real case applications (i.e., FEW system
planning, policy, management). This decision science perspective requires linking
modeling with decision-making through a recursive, iterative process of coproduction and adaptive management.
It is increasingly common for model developers to consider multiple stakeholders’ perspectives in the design process in an effort to enhance the relevance and
usability of models and decision support systems. For instance, research has examined how modelers themselves frame key concepts such as sustainability in the
design process, how multiple stakeholder groups interpret models and visualizations, the factors affecting the adoption of models for decision-making purposes,
and decision-maker perceptions of how uncertainty is treated and visualized in
models and visualizations. Such research reflects a growing awareness of the importance of understanding the way that individual actors and social groups frame issues
and how their perspectives play out through social processes in participatory, integrated resources management. This approach was developed, studied, and refined
through sustained engagement between scientists and resource management stakeholders; the process involved data collection through interviews, focus groups,
questionnaires, participant observations, and document analysis. See Sect. 11.3 for
a description of the methodology developed by the Intergovernmental Panel on
Climate Change for communicating risk, probability, and scientific uncertainty.
The co-development process is central to the successful application of Nexus
science to real-world decision-making (e.g., Lubchenco 1998). Prior research has
demonstrated empirically that the degree to which scientific and technical knowledge is successfully applied to decision-making depends on three criteria:
• salience: significance and impact;
• credibility: trusted by key stakeholders; and
• legitimacy: recognized as conforming to the rules of high-quality science such as
reproducibility and peer review.
The degree to which these criteria are maximized is determined by the level of
joint participation by multiple scientific disciplines and stakeholders, accountability
to scientific and policy concerns, and production of models, decision support systems, and other “boundary objects” that serve as points of articulation to mediate the
interactions between scientists and decision-makers.
Two-way, iterative engagement between producers and users of scientific information is also key to building trust and to better understanding the needs of policy
and what scientists can provide to assist policy formulation. It has also been empirically demonstrated that collaborative processes that facilitate social learning and the
co-production of knowledge can help overcome concerns about credibility, saliency,
legitimacy of research, and related boundary objects. First, perceived credibility and
legitimacy can be enhanced as individual concerns are heard, and collective decisions made to improve the research and information. Second, opportunities for privately or confidentially expressing opinions can be important when group settings
can inhibit the sharing of controversial viewpoints. Third, collaborative modeling
E. Eftelioglu et al.
To address these interrelated challenges, processes that are decision-driven and
participatory have been co-developed and implemented, which make research more
responsive to decision-making needs in real case applications (i.e., FEW system
planning, policy, management). This decision science perspective requires linking
modeling with decision-making through a recursive, iterative process of coproduction and adaptive management.
It is increasingly common for model developers to consider multiple stakeholders’ perspectives in the design process in an effort to enhance the relevance and
usability of models and decision support systems. For instance, research has examined how modelers themselves frame key concepts such as sustainability in the
design process, how multiple stakeholder groups interpret models and visualizations, the factors affecting the adoption of models for decision-making purposes,
and decision-maker perceptions of how uncertainty is treated and visualized in
models and visualizations. Such research reflects a growing awareness of the importance of understanding the way that individual actors and social groups frame issues
and how their perspectives play out through social processes in participatory, integrated resources management. This approach was developed, studied, and refined
through sustained engagement between scientists and resource management stakeholders; the process involved data collection through interviews, focus groups,
questionnaires, participant observations, and document analysis. See Sect. 11.3 for
a description of the methodology developed by the Intergovernmental Panel on
Climate Change for communicating risk, probability, and scientific uncertainty.
The co-development process is central to the successful application of Nexus
science to real-world decision-making (e.g., Lubchenco 1998). Prior research has
demonstrated empirically that the degree to which scientific and technical knowledge is successfully applied to decision-making depends on three criteria:
• salience: significance and impact;
• credibility: trusted by key stakeholders; and
• legitimacy: recognized as conforming to the rules of high-quality science such as
reproducibility and peer review.
The degree to which these criteria are maximized is determined by the level of
joint participation by multiple scientific disciplines and stakeholders, accountability
to scientific and policy concerns, and production of models, decision support systems, and other “boundary objects” that serve as points of articulation to mediate the
interactions between scientists and decision-makers.
Two-way, iterative engagement between producers and users of scientific information is also key to building trust and to better understanding the needs of policy
and what scientists can provide to assist policy formulation. It has also been empirically demonstrated that collaborative processes that facilitate social learning and the
co-production of knowledge can help overcome concerns about credibility, saliency,
legitimacy of research, and related boundary objects. First, perceived credibility and
legitimacy can be enhanced as individual concerns are heard, and collective decisions made to improve the research and information. Second, opportunities for privately or confidentially expressing opinions can be important when group settings
can inhibit the sharing of controversial viewpoints. Third, collaborative modeling
E. Eftelioglu et al.