부산대학교 도서관

In ecological and environmental contexts, management actions must sometimes be chosen urgently. Value of information (VoI) analysis provides a quantitative toolkit for projecting the improved management outcomes expected after making additional measurements. However, traditional VoI analysis reports metrics as expected values (i.e.risk-neutral). This can be problematic because expected values hide uncertainties in projections. The true value of a measurement will only be known after the measurement’s outcome is known, leaving large uncertainty in the measurement’s value before it is performed. As a result, the expected value metrics produced in traditional VoI analysis may not align with the priorities of a risk-averse decision-maker who wants to avoid low-value measurement outcomes. In the present work, we introduce four new VoI metrics that can address a decision-maker’s risk-aversion to different measurement outcomes. We demonstrate the benefits of the new metrics with two ecological case studies for which traditional VoI analysis has been previously applied. In the first case study concerning a test for disease presence at a potential frog translocation site, traditional VoI analysis predicts the test yields an additional expected gain of approximately 10 frogs. However, our new VoI metrics also highlight a 40% risk that the test is valueless; this knowledge may deter a risk-averse decision-maker from doing the test. In the second case study concerning the design of a trial release prior to a large-scale turtle reintroduction, traditional and new VoI metrics have consistent predictions of which design to choose. However, whilst the best trial release design will increase expected turtle survival in the wild by only 3%, the new VoI metrics find that this trial design has a 94% probability of improving the design of the large-scale turtle reintroduction. Using the new metrics, we also demonstrate a clear mathematical link between the often-separated environmental decision-making disciplines of VoI and optimal design of experiments. This mathematical link has the potential to catalyse future collaborations between ecologists and statisticians to work together to quantitatively address environmental decision-making questions of fundamental importance. Overall, the introduced VoI metrics complement existing metrics to provide decision-makers with a comprehensive view of the value of, and risks associated with, a proposed monitoring or measurement activity. This is critical for improved environmental outcomes when decisions must be urgently made.