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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Adaptive managementDecision elementsDouble-loop learningTechnical and institutional learningUncertainty

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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.