Validation, Verification, and Uncertainty Quantification for Models with Intelligent Adversaries

Jing Zhang () and Jun Zhuang ()
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Jing Zhang: New York State University at Buffalo, Department of Industrial and Systems Engineering
Jun Zhuang: New York State University at Buffalo, Department of Industrial and Systems Engineering

Chapter 41 in Handbook of Uncertainty Quantification, 2017, pp 1401-1419 from Springer

Abstract: Abstract Model verification and validation (V&V) are essential before a model can be implemented in practice. Integrating model V&V into the process of model development can help reduce the risk of errors, enhance the accuracy of the model, and strengthen the confidence of the decision-maker in model results. Besides V&V, uncertainty quantification (UQ) techniques are used to verify and validate computational models. Modeling intelligent adversaries is different from and more difficult than modeling non-intelligent agents. However, modeling intelligent adversaries is critical to infrastructure protection and national security. Model V&V and UQ for intelligent adversaries present a big challenge. This chapter first reviews the concepts of model V&V and UQ in the literature and then discusses model V&V and UQ for intelligent adversaries. Some V&V techniques for modeling intelligent adversaries are provided which could be beneficial to model developers and decision-makers facing with intelligent adversaries.

Keywords: Decision making; Intelligent adversaries; Model validation and verification; Validation techniques (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-12385-1_44

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DOI: 10.1007/978-3-319-12385-1_44

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