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Sustainability scientists are developing new knowledge production processes (KPPs) based on findings that science has a greater impact on decision-making when it (1) adopts an interdisciplinary systems approach, and (2) is participatory and, therefore, perceived as more salient, legitimate, and credible by users. This presentation will discuss the findings from a review of the literature on the intersection of two KPP methods: systems dynamics (SD) and role-play simulations (RPS).

SD is a powerful approach for modeling dynamic, complex systems to improve understanding of system behaviors in coupled social-ecological systems. It can capture complex biophysical phenomena and trade-offs, while also representing feedbacks and thresholds from social and institutional systems. It incorporates both qualitative and quantitative information. Unlike static models, SD is explicitly dynamic. It is well suited to group modeling efforts and informing consensus-based decisions. RPSs are experiential, scenario-based tools that help participants learn about how science is used in policy-making decisions, learn about others' preferences and priorities regarding a public policy decision, develop and evaluate innovative options for addressing critical challenges, and contribute to building consensus among diverse and interdependent stakeholders.

Although both approaches aim to improve the basis for decision-making, they are rarely discussed together. This presentation considers the literature on each method and their intersection by analyzing: (1) each method's objectives and functions, (2) the steps in their processes for incorporating participation and interdisciplinary, systems-based knowledge, (3) approaches for evaluating outcomes, (4) strengths and weaknesses, (5) opportunities and challenges for integrations, and identifies recommendations for future research.

A version of the presentation with an attached transcript can be found here.

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Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement Program NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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