Quantifying the multi-objective cost of uncertainty

TL;DR

This paper introduces a new metric called multi-objective MOCU to quantify how model uncertainty impacts multiple operational objectives in complex systems, aiding in optimal experiment design.

Contribution

The paper proposes the multi-objective MOCU, a novel approach for quantifying uncertainty effects on multiple objectives, with illustrative examples and a real-world application.

Findings

Multi-objective MOCU effectively quantifies uncertainty impact on multiple objectives.

The approach helps prioritize experiments to reduce uncertainty where it matters most.

Application to cell cycle network demonstrates practical utility.

Abstract

Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.