Managing and learning with multiple models: Objectives and optimization algorithms

Biological Conservation
By: , and 



The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.
Publication type Article
Publication Subtype Journal Article
Title Managing and learning with multiple models: Objectives and optimization algorithms
Series title Biological Conservation
DOI 10.1016/j.biocon.2010.07.031
Volume 144
Issue 4
Year Published 2011
Language English
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Biological Conservation
First page 1237
Last page 1245
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