On Valuing Information in Adaptive‐Management Models

Abstract

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.