School of Botany, University of Melbourne, Parkville, Victoria 3010, Australia.
Conserv Biol. 2010 Aug;24(4):984-93. doi: 10.1111/j.1523-1739.2009.01443.x. Epub 2010 Feb 4.
Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive-management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay-off for learning about their benefits is large.
主动自适应管理着眼于使用可能在短期内不太理想但可能提供更多信息的策略的好处，这些信息将有助于未来更好地管理。在许多已研究的自适应管理问题中，最优的主动和被动策略（分别考虑在设计策略时的学习和根据当前最佳信息设计策略）非常相似。这似乎自相矛盾；当面临最佳行动方案的不确定性时，管理者应该在积极设计方案以了解他们正在管理的系统方面投入很少的精力。我们考虑了主动和被动自适应解决方案通常相似的两个可能原因。首先，学习的好处通常局限于建模场景中的特定案例研究，而实际上从局部研究中获得的信息通常更广泛地应用。其次，纳入估计方差的管理目标可能比通常旨在最大化预期值的目标更强调学习。我们在澳大利亚墨尔本 Merri Creek 的案例研究中探讨了这些问题，该案例旨在选择两种植被恢复选项。我们在模型中明确纳入了监测成本。终端奖励的价值和目标选择都影响了主动和被动自适应解决方案之间的差异。明确考虑监测成本提供了一个不同的视角，了解终端奖励和管理目标如何影响学习。最优的监测状态并不总是与主动和被动自适应管理不同的状态相吻合。我们的结果强调，只有当所考虑的选项的预期收益相似且了解其收益的学习回报较大时，投入资源进行监测才是最优的。
