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In ecological and environmental contexts, management actions must sometimesbe chosen urgently. Value of information (VoI) analysis provides a quantitativetoolkit for projecting the expected improvement that additional measurementsmake to the choice of management actions. However, traditional VoI analysisreports metrics as expected values (i.e. risk-neutral). This can be problematicbecause expected values hide uncertainties in projections. The true value of ameasurement will only be known after the measurement's outcome is known; therecan be large uncertainty in the measurement's value before its outcome isknown. As a result, the expected value metrics produced in traditional VoIanalysis may not align with the priorities of a risk-averse decision-maker whowants to avoid low-value measurement outcomes. In the present work, weintroduce four new VoI metrics that can address risk aversion of thedecision-maker to different measurement outcomes. We demonstrate the benefitsof the new metrics with two ecological case studies for which traditional VoIanalysis has been previously applied. Using the new metrics, we alsodemonstrate a clear mathematical link between the often-separated environmentaldecision-making disciplines of VoI and optimal design of experiments. Thismathematical link has the potential to catalyse future collaborations betweenecologists and statisticians to work together to quantitatively addressenvironmental decision-making questions of fundamental importance. Overall, theintroduced VoI metrics complement existing metrics to provide decision-makerswith a comprehensive view of the value of, and risks associated with, aproposed monitoring or measurement activity. This is critical for improvedenvironmental outcomes when decisions must be urgently made.

Beyond expected values: Making environmental decisions using value of information analysis when measurement outcome matters