Sensitivity measures for engineering and environmental decision support

TL;DR

This paper reviews the concept of information value as a sensitivity measure for decision support in engineering and environmental contexts, emphasizing its ability to provide both relative and absolute insights into uncertainty reduction.

Contribution

It offers a comprehensive overview of information value theory and methods, including differentiation of uncertainty types and application to continuous decision parameters.

Findings

Demonstrates how to differentiate between aleatory and epistemic uncertainty.

Introduces evaluation methods for continuous decision parameters.

Provides real-life case studies illustrating decision support applications.

Abstract

Information value, a measure for decision sensitivity, can provide essential information in engineering and environmental assessments. It quantifies the potential for improved decision-making when reducing uncertainty in specific inputs. By contrast to other sensitivity measures, it admits not only a relative ranking of input factors but also an absolute interpretation through statements like ''Eliminating the uncertainty in factor $A$ has an expected value of $5000$ Euro''. In this paper, we present a comprehensive overview of the information value by presenting the theory and methods in view of their application to engineering and environmental assessments. We show how one should differentiate between aleatory and epistemic uncertainty in the analysis. Furthermore, we introduce the evaluation of the information value in applications where the decision is described by a continuous parameter. The paper concludes with two real-life applications of the information value to highlight its power in supporting decision-making in engineering and environmental applications.