The goal of this project is to educate resource professionals in the tools and techniques of structured decision making and adaptive management.
The Science Issue and Relevance: Researchers and managers often find it difficult to work together to design useful decision-support tools. This difficulty often stems from a failure to structure decision processes in a systematic way, i.e., bounding and focusing the debate over choices, outcomes, and values. This goal of this project is to educate resource professionals in the tools and techniques of structured decision making and adaptive management.
Methods for Addressing the Issue: In collaboration with the University of Florida, we will develop a curriculum in decision analysis that can be taken by graduate student and resource professionals. We will also contribute to the development of learning aids for researchers and managers interested in applying decision-analytic approaches (e.g., books, workshops, PowerPoint presentations, journal articles).
Future Steps: One introductory course has been developed and offered in resource decision-making via distance learning at the University of Florida. Several manuscripts are being developed in collaboration with the Cooperative Research Units and Patuxent Wildlife Research Center to expand the theory of decision science as it pertains to concepts of resilient ecosystems and robust decision making under severe uncertainty.
Below are publications associated with this project.
Value of information and natural resources decision-making
Training conservation practitioners to be better decision makers
A decision-analytic approach to adaptive resource management
Optimization and resilience in natural resources management
Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?
Confronting dynamics and uncertainty in optimal decision making for conservation
- Overview
The goal of this project is to educate resource professionals in the tools and techniques of structured decision making and adaptive management.
The Science Issue and Relevance: Researchers and managers often find it difficult to work together to design useful decision-support tools. This difficulty often stems from a failure to structure decision processes in a systematic way, i.e., bounding and focusing the debate over choices, outcomes, and values. This goal of this project is to educate resource professionals in the tools and techniques of structured decision making and adaptive management.
Methods for Addressing the Issue: In collaboration with the University of Florida, we will develop a curriculum in decision analysis that can be taken by graduate student and resource professionals. We will also contribute to the development of learning aids for researchers and managers interested in applying decision-analytic approaches (e.g., books, workshops, PowerPoint presentations, journal articles).
Future Steps: One introductory course has been developed and offered in resource decision-making via distance learning at the University of Florida. Several manuscripts are being developed in collaboration with the Cooperative Research Units and Patuxent Wildlife Research Center to expand the theory of decision science as it pertains to concepts of resilient ecosystems and robust decision making under severe uncertainty.
- Publications
Below are publications associated with this project.
Value of information and natural resources decision-making
Though the potential for information to measurably improve management has been highlighted for several decades, in recent years the “value of information” has surfaced with increasing frequency in natural resources. However, the use of this phrase belies the fact that many in natural resources have only a limited understanding about what it actually means, how to measure it, and what to do with itAuthorsByron K. Williams, Fred A. JohnsonTraining conservation practitioners to be better decision makers
Traditional conservation curricula and training typically emphasizes only one part of systematic decision making (i.e., the science), at the expense of preparing conservation practitioners with critical skills in values-setting, working with decision makers and stakeholders, and effective problem framing. In this article we describe how the application of decision science is relevant to conservatiAuthorsFred A. Johnson, Mitchell J. Eaton, James H. Williams, Gitte H. Jensen, Jesper MadsenA decision-analytic approach to adaptive resource management
No abstract available.AuthorsFred A. Johnson, Byron K. WilliamsOptimization and resilience in natural resources management
We consider the putative tradeoff between optimization and resilience in the management of natural resources, using a framework that incorporates different sources of uncertainty that are common in natural resources management. We address one-time decisions, and then expand the decision context to the more complex problem of iterative decision making. For both cases we focus on two key sources ofAuthorsByron K. Williams, Fred A. JohnsonResilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?
There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is heldAuthorsFred A. Johnson, Byron K. Williams, James D. NicholsConfronting dynamics and uncertainty in optimal decision making for conservation
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a geAuthorsByron K. Williams, Fred A. Johnson