How we value the future affects our desire to learn.
Active adaptive management is increasingly advocated in natural resource management and conservation biology. Active adaptive management looks at the benefit of employing strategies that may be suboptimal in the near term but which may provide additional information that will facilitate better management in future years. However, when comparing management policies it is traditional to weigh future rewards geometrically (at a constant discount rate) which results in far-distant rewards making a negligible contribution to the total benefit. Under such a discounting scheme active adaptive management is rarely of much benefit, especially if learning is slow. A growing number of authors advocate the use of alternative forms of discounting when evaluating optimal strategies for long-term decisions which have a social component. We consider a theoretical harvested population for which the recovery rate from an unharvestably small population size is unknown and look at the effects on the benefit of experimental management when three different forms of discounting are employed. Under geometric discounting, with a discount rate of 5% per annum, managing to learn actively had little benefit. This study demonstrates that discount functions which weigh future rewards more heavily result in more conservative harvesting strategies, but do not necessarily encourage active learning. Furthermore, the optimal management strategy is not equivalent to employing geometric discounting at a lower rate. If alternative discount functions are made mandatory in calculating optimal management strategies for environmental management then this will affect the structure of optimal management regimes and change when and how much we are willing to invest in learning.
Moore
Alana L
AL
Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria 3010, Australia. a.moore@ms.unimelb.edu.au
Hauser
Cindy E
CE
McCarthy
Michael A
MA
D003247:Conservation of Natural Resources; D005544:Forecasting; D007858:Learning; D008954:Models, Biological; D011156:Population Density; D013997:Time Factors