WK 2 - An Introduction to Structured Decision Making for Natural Resources Management

Saturday, August 5, 2017: 8:00 AM-5:00 PM
A106, Oregon Convention Center
Organizer:
James T. Peterson, USGS Oregon Cooperative Fish and Wildlife Research Unit, Oregon State University
Co-organizer:
Adam Duarte, Oregon Cooperative Fish and Wildlife Research Unit, Oregon State University
Natural resource managers often face difficult decisions on how to satisfy the socio-economic needs of the public while conserving or restoring ecological systems. Such decisions are fraught with the complexity and uncertainty associated with ecological system dynamics and multiple, potentially conflicting, objectives under consideration. To aid in the decision-making process, the decision sciences have developed approaches that allow decision makers to: examine the expected outcome of different strategies before implementation; incorporate multiple objectives and values of stakeholders; determine the relative influence of various sources of uncertainty; and estimate the value of collecting additional data. Adaptive management, a special case of decision analysis, is used to reduce uncertainties through monitoring, increasing the value of management. Notably, these approaches provide a framework that is transparent, adaptable, and ideal for interdisciplinary management teams to cooperate and create the most effective management strategies. Despite the potential advantages, structured decision making is not widely applied in natural resource management, with the exception of a few notable conservation efforts. To this point, a primary impediment to the broad-scale application of decision analysis has been a lack of training opportunities for natural resource professionals in the concepts and methodology. Our primary aim in this course is to provide participants from diverse backgrounds a comprehensive understanding of the structured decision making process and techniques in a format that does not require extensive quantitative understanding and skills. The course will consist of both lectures and computer laboratory exercises.

Registration Fee: $35

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