Managing and learning with multiple models: Objectives and optimization algorithms

Probert, W. J. M., Hauser, C. E., McDonald-Madden, E., Runge, M. C., Baxter, P. W. J., & Possingham, H. P. (2011) Managing and learning with multiple models: Objectives and optimization algorithms. Biological Conservation, 144(4), pp. 1237-1245.

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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.

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ID Code: 82394
Item Type: Journal Article
Refereed: Yes
Keywords: Adaptive management, Conservation biology, Decision theory, Optimization, Stochastic dynamic programming, Uncertainty, algorithm, conservation management, decision making, environmental monitoring, management practice, numerical model, population growth, stochasticity, uncertainty analysis
DOI: 10.1016/j.biocon.2010.07.031
ISSN: 00063207 (ISSN)
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Elsevier
Deposited On: 10 Mar 2015 07:14
Last Modified: 13 Mar 2015 05:34

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