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Double-Loop Learning in Adaptive Management: The Need, the Challenge, and the Opportunity
Byron K Williams1,2, Eleanor D Brown3
1The Wildlife Society, 425 Barlow Place, Suite 200, Bethesda, MD, 20814, USA.
Environmental Management
|October 1, 2018
Summary
Adaptive management uses iterative learning to address uncertainty in resource dynamics and decision-making processes. This study introduces a framework for institutional learning about decision elements, complementing technical learning for better resource management.
Area of Science:
- Environmental Science
- Resource Management
- Ecology
Background:
- Adaptive management aims to reduce uncertainty in ecological and resource dynamics.
- Current approaches often focus on structural uncertainty, with less emphasis on decision-making elements.
- Institutional learning regarding decision elements is crucial for effective adaptive management.
Purpose of the Study:
- To describe evidence-based institutional learning about decision elements in adaptive management.
- To present a framework for institutional learning that complements technical learning.
- To enhance the adaptive management process for renewable natural resources.
Main Methods:
- Described adaptive management as a multi-phase approach: set-up, iterative technical learning, and institutional learning.
- Proposed a framework for institutional learning including uncertainty metrics and change propagation.
- Discussed operational aspects of recognizing and adjusting decision elements.
Main Results:
- Adaptive management involves distinct phases for technical and institutional learning.
- A framework for institutional learning was developed, focusing on decision elements.
- The framework addresses when and how to revisit and adjust decision architecture.
Conclusions:
- Learning about decision architecture is as critical as learning about resource dynamics.
- The proposed framework supports evidence-based institutional learning in adaptive management.
- This approach is applicable to decision-making for renewable natural resources.
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