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. 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.

翻译：在生态和环境背景下，管理行动有时必须紧急选择。信息价值（VoI）分析提供了一种量化工具包，用于预测在进行额外测量后预期能获得的改进管理成果。然而，传统的VoI分析以期望值（即风险中性）报告指标，这可能存在问题，因为期望值掩盖了预测中的不确定性。测量的真实价值只有在得知测量结果后才会知晓，因此在测量执行前，其价值存在巨大不确定性。因此，传统VoI分析中产生的期望值指标可能无法与风险规避型决策者的优先级一致，后者希望避免低价值的测量结果。在本研究中，我们引入了四种新的VoI指标，能够应对决策者对不同测量结果的风险规避。通过两个此前已应用过传统VoI分析的生态案例研究，我们展示了新指标的优势。利用新指标，我们还展示了VoI与实验优化设计这两个常被分离的环境决策学科之间的清晰数学联系。这种数学联系有可能促进生态学家与统计学家之间的未来合作，共同量化解决具有根本重要性的环境决策问题。总体而言，所引入的VoI指标补充了现有指标，为决策者提供了对拟议监测或测量活动的价值及相关风险的综合视角。当决策必须紧急做出时，这对于改善环境结果至关重要。