Quantifying the Multi-Objective Cost of Uncertainty

Journal Article · 02 June 2021 · IEEE Access

DOI: https://doi.org/10.1109/access.2021.3085486 · OSTI ID:1853045

Yoon, Byung-Jun  ^[1]; Qian, Xiaoning  ^[2]; Dougherty, Edward R. ^[2]

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1. Texas A & M Univ., College Station, TX (United States); Brookhaven National Lab. (BNL), Upton, NY (United States); Texas A & M Univ., College Station, TX (United States)
2. Texas A & M Univ., College Station, TX (United States)

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.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States); Univ. of Texas, Austin, TX (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Science (SC)
• Grant/Contract Number: SC0019303
• OSTI ID: 1853045
• Journal Information: IEEE Access, Journal Name: IEEE Access Vol. 9; ISSN 2169-3536
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English