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

In: Handbook of Uncertainty Quantification

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jing Zhang & Jun Zhuang, 2017. "Validation, Verification, and Uncertainty Quantification for Models with Intelligent Adversaries," Springer Books, in: Roger Ghanem & David Higdon & Houman Owhadi (ed.), Handbook of Uncertainty Quantification, chapter 41, pages 1401-1419, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-12385-1_44
    DOI: 10.1007/978-3-319-12385-1_44
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