Knowledge gain or system benefit in environmental decision making?

Probert, W.J.M., McDonald-Madden, E., Hauser, C.E., Baxter, P.W.J. and Possingham, H.P. (2009). Knowledge gain or system benefit in environmental decision making?. In: R. S. Anderssen, R. D. Braddock and L. T. H. Newham, Proceedings of the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. 18 World IMACS / MODSIM Congress, Cairns, Australia, (1788-1794). 13-17July 2009.


Author Probert, W.J.M.
McDonald-Madden, E.
Hauser, C.E.
Baxter, P.W.J.
Possingham, H.P.
Title of paper Knowledge gain or system benefit in environmental decision making?
Conference name 18 World IMACS / MODSIM Congress
Conference location Cairns, Australia
Conference dates 13-17July 2009
Convener David Mayer & Neil Gribble
Proceedings title Proceedings of the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation
Journal name 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings
Place of Publication Canberra, Australia
Publisher Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation
Publication Year 2009
Year available 2009
Sub-type Fully published paper
ISBN 978-0-9758400-7-8
Editor R. S. Anderssen
R. D. Braddock
L. T. H. Newham
Volume 1
Start page 1788
End page 1794
Total pages 7
Language eng
Abstract/Summary 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.
Subjects E1
970101 Expanding Knowledge in the Mathematical Sciences
010202 Biological Mathematics
Keyword adaptive management
conservation biology
decision theory
optimal monitoring
optimization
stochastic dynamic programming
Q-Index Code E1
Q-Index Status Confirmed Code

 
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Created: Thu, 13 May 2010, 01:47:18 EST by Gail Walter on behalf of School of Biological Sciences