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Nonlinear Monte Carlo Methodology

In: Risk-Based Project Decisions in Situations of High Complexity and Deep Uncertainty

Author

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  • Yuri G. Raydugin

    (Risk Services & Solutions Inc.)

Abstract

The introduction of the three bowtie diagrams for risk interactions and frameworks for project system maturity evaluation in the previous chapter opens a door to the quantification of risk interactions. The challenge is just to convert the newly developed bowtie diagrams to standard mathematical expressions for the three types of risk interactions. After this qualitative assessment, chronic and cross-risk system issues standing behind the risk interactions shall be consistently converted to quantitative estimates. As a result, these mathematical expressions should be made up of standalone risks coming from the linear Monte Carlo modelling, although weighted with pertaining nonlinearity multipliers. The two versions of the Monte Carlo methodology—linear and nonlinear—shall be recognized as inseparable twins: the nonlinear version has to intake the results of the linear modelling, including a model itself as a starting point. Thus, the initially linear Monte Carlo methodology becomes a workable nonlinear Monte Carlo one. A simplistic business case demonstrating features of nonlinear Monte Carlo modelling concludes the chapter.

Suggested Citation

  • Yuri G. Raydugin, 2024. "Nonlinear Monte Carlo Methodology," Springer Books, in: Risk-Based Project Decisions in Situations of High Complexity and Deep Uncertainty, chapter 0, pages 259-289, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-56988-3_9
    DOI: 10.1007/978-3-031-56988-3_9
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