# Quantifying the multi-objective cost of uncertainty

arXiv: https://arxiv.org/abs/2010.04653 · HTML: https://mlsanity.com/paper/2010.04653/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Multi-Objective Latent Space Optimization of Generative Molecular Design Models](https://mlsanity.com/paper/2203.00526/) (2022)
- [Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning](https://mlsanity.com/paper/2109.02150/) (2021)
- [A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows](https://mlsanity.com/paper/2301.05763/) (2023)
- [Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design](https://mlsanity.com/paper/2203.07120/) (2022)
- [Cost-effective Reduced-Order Modeling via Bayesian Active Learning](https://mlsanity.com/paper/2506.22645/) (2025)

## Abstract (arXiv)

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.