Quantifying the multi-objective cost of uncertainty
This work addresses the challenge of designing optimal experiments to reduce uncertainty affecting multiple objectives in real-world applications, such as biological networks, but it appears incremental as it extends existing uncertainty quantification methods to multi-objective scenarios.
The paper tackles the problem of quantifying the impact of model uncertainty on multiple operational objectives in complex systems, proposing a concept called mean multi-objective cost of uncertainty (multi-objective MOCU) and demonstrating its application through illustrative examples and a real-world case study on a mammalian cell cycle network.
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.