Ecological Applications, 18(4), 2008, pp. 1061–1069 Ó 2008 by the Ecological Society of America HOW WE VALUE THE FUTURE AFFECTS OUR DESIRE TO LEARN ALANA L. MOORE, 1,4 CINDY E. HAUSER, 2 AND MICHAEL A. MCCARTHY 3 1 Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria 3010 Australia 2 Australian Centre of Excellence for Risk Analysis, School of Botany, University of Melbourne, Parkville, Victoria 3010 Australia 3 School of Botany, University of Melbourne, Parkville, Victoria 3010 Australia Abstract. 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 environ- mental management then this will affect the structure of optimal management regimes and change when and how much we are willing to invest in learning. Key words: adaptive management; discounting; experimental management; geometric; hyperbolic; intergenerational; learning. INTRODUCTION Recently, much attention has been paid to the benefit of employing active adaptive management strategies in natural resource management (Walters and Hilborn 1978, Smith and Walters 1981, Shea et al. 2002, Hauser and Possingham 2007, McCarthy and Possingham 2007). Active adaptive management looks at the benefit of employing strategies that may be suboptimal in the near future but that may provide additional information that will facilitate better management in future years. Several authors have applied this approach to optimal harvest strategies, usually with applications to fisheries. For example, Silvert (1978) examined passive and active optimal control strategies in managing a fishery when it is unknown which of two recruitment curves is correct. Hauser and Possingham (2007) considered a theoretical harvested population for which the recovery rate from small populations was uncertain. When comparing costs and benefits of various management regimes through time it is common to calculate a net present value of employing a particular strategy by taking the weighted sum of all rewards over the time period under consideration. The traditional method of discounting is to discount future rewards at a constant rate, which, in discrete time, leads to a geometric discount function. A constant discount rate is necessary under the assumption of stationarity, which requires that whichever strategy is determined to be optimal today will still be optimal if recalculated at some future time, given the same information is available (e.g., Koopmans 1960, Winkler 2006). However, discounting future rewards geometrically results in rewards earned in the distant future making a negligible contribution to the total net present value under a given regime. Consequently, geometric discounting tends to favor unsustainable management policies. In the context of adaptive management (AM), active probing in order to learn about the system becomes unbeneficial; especially if the benefits of learning will not be realized until the distant future, which is often the case when managing ecological systems. Silvert (1978) derived a critical discount factor below which it is optimal to retain the current management plan, even though it is suboptimal for each alternative hypothesis of reality. Hauser and Possingham (2007) found that over a long time horizon, a moderate discount rate of roughly 5% per annum resulted in negligible benefit of active over passive AM. Manuscript received 17 May 2007; accepted 12 December 2007. Corresponding Editor: D. S. Schimel. 4 E-mail: a.moore@ms.unimelb.edu.au 1061