Knowledge gain or system benefit in environmental decision making?

Probert, W. J. M., McDonald-Madden, E., Hauser, C. E., Baxter, P. W. J., & Possingham, H. P. (2009) Knowledge gain or system benefit in environmental decision making? In Anderssen, R. S., Braddock, R., & Newham, L.T.H. (Eds.) Proceedings of the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, Cairns, Australia, pp. 1788-1794.

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Abstract

The quality of environmental decisions are gauged according to the management objectives of a conservation project. Management objectives are generally about maximising some quantifiable measure of system benefit, for instance population growth rate. They can also be defined in terms of learning about the system in question, in such a case actions would be chosen that maximise knowledge gain, for instance in experimental management sites. Learning about a system can also take place when managing practically. The adaptive management framework (Walters 1986) formally acknowledges this fact by evaluating learning in terms of how it will improve management of the system and therefore future system benefit. This is taken into account when ranking actions using stochastic dynamic programming (SDP). However, the benefits of any management action lie on a spectrum from pure system benefit, when there is nothing to be learned about the system, to pure knowledge gain. The current adaptive management framework does not permit management objectives to evaluate actions over the full range of this spectrum. By evaluating knowledge gain in units distinct to future system benefit this whole spectrum of management objectives can be unlocked. This paper outlines six decision making policies that differ across the spectrum of pure system benefit through to pure learning. The extensions to adaptive management presented allow specification of the relative importance of learning compared to system benefit in management objectives. Such an extension means practitioners can be more specific in the construction of conservation project objectives and be able to create policies for experimental management sites in the same framework as practical management sites.

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ID Code: 82392
Item Type: Conference Paper
Refereed: Yes
Keywords: Adaptive management, Conservation biology, Decision theory, Optimal monitoring, Optimization, Stochastic dynamic programming, Conservation projects, Environmental decision making, Environmental decisions, Management objectives, Population growth rates, Quantifiable measures, Relative importance, Conservation, Dynamic programming, Environmental protection, Mathematical models, Population statistics, Project management, Stochastic systems, Decision making
ISBN: 9780975840078 (ISBN)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 10 Mar 2015 07:57
Last Modified: 24 Jun 2017 07:01

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