Sensitivity measures for engineering and environmental decision support

2025-07-11

AI Summary

This study addresses the impact of input uncertainty on decision quality in engineering and environmental decision-making. We propose a sensitivity analysis framework centered on the Value of Information (VoI) as the core metric. Methodologically, we distinguish aleatory from epistemic uncertainty and develop a decision-theoretic VoI quantification approach grounded in continuous-parameter probabilistic models, yielding absolute economic interpretations (e.g., “resolving uncertainty in factor A increases expected utility by €5,000”). Our key contributions are: (i) the first systematic integration of VoI into continuous decision spaces, and (ii) differentiable, interpretable modeling and attribution of uncertainty sources. Validation across two real-world case studies demonstrates substantial improvements in decision support accuracy, transparency, and practical utility, providing rigorous, quantitative guidance for uncertainty-driven prioritization of interventions.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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.

Problem

• Quantifies decision-making improvement by reducing input uncertainty
• Differentiates between aleatory and epistemic uncertainty types
• Applies information value to continuous parameter decisions

Innovation

• Quantifies decision improvement via uncertainty reduction
• Differentiates aleatory and epistemic uncertainty types
• Extends information value to continuous parameter decisions