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

TL;DR

This paper introduces four new value of information metrics tailored for risk-averse environmental decision-making, addressing the limitations of traditional expected-value-based VoI analysis by incorporating measurement outcome uncertainties.

Contribution

It develops risk-sensitive VoI metrics and establishes a mathematical link between VoI and optimal experimental design, enhancing decision-making in urgent ecological contexts.

Findings

New VoI metrics better capture measurement outcome uncertainties.

Demonstrated benefits through two ecological case studies.

Established a link between VoI and optimal experimental design.

Abstract

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.